(2024) 22:334
González‑Gil et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03544-3

BMC Medicine

RESEARCH

Open Access

Association of body shape phenotypes
and body fat distribution indexes
with inflammatory biomarkers in the European
Prospective Investigation into Cancer
and Nutrition (EPIC) and UK Biobank
Esther M. González‑Gil1*†, Laia Peruchet‑Noray1,2†, Anja M. Sedlmeier3,4,5, Sofia Christakoudi6, Carine Biessy1,
Anne‑Sophie Navionis1, Yahya Mahamat‑Saleh1, Rola F. Jaafar1, Hansjörg Baurecht3, Marcela Guevara7,8,9,
Pilar Amiano Etxezarreta8,10,11, W. M. Monique Verschuren12,13, Jolanda M. A. Boer12, Anja Olsen14,15,
Anne Tjønneland14,16, Vittorio Simeon17, Carlota Castro‑Espin18,19, Dagfinn Aune6,20,21, Alicia K. Heath6,
Marc Gunter6, Sandra M. Colorado‑Yohar8,22,23, Nuno R. Zilhão24, Christina C. Dahm24, Erand Llanaj25,26,
Matthias B. Schulze25,27, Dafina Petrova8,28,29, Sabina Sieri30, Fulvio Ricceri31, Giovanna Masala32, Tim Key33,
Vivian Viallon1, Sabina Rinaldi1, Heinz Freisling1 and Laure Dossus1

Abstract
Background The allometric body shape index (ABSI) and hip index (HI), as well as multi-trait body shape phenotypes,
have not yet been compared in their associations with inflammatory markers. The aim of this study was to examine
the relationship between novel and traditional anthropometric indexes with inflammation using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts.
Methods Participants from EPIC (n = 17,943, 69.1% women) and UK Biobank (n = 426,223, 53.2% women) with dataon anthropometric indexes and C-reactive protein (CRP) were included in this cross-sectional analysis. A subset
of women in EPIC also had at least one measurement for interleukins, tumour necrosis factor alpha, interferon
gamma, leptin, and adiponectin. Four distinct body shape phenotypes were derived by a principal component(PC) analysis on height, weight, body mass index (BMI), waist (WC) and hip circumferences (HC), and waist-to-hip
ratio (WHR). PC1 described overall adiposity, PC2 tall with low WHR, PC3 tall and centrally obese, and PC4 high BMI
and weight with low WC and HC, suggesting an athletic phenotype. ABSI, HI, waist-to-height ratio and waist-to-hip
index (WHI) were also calculated. Linear regression models were carried out separately in EPIC and UK Biobank stratified by sex and adjusted for age, smoking status, education, and physical activity. Results were additionally combinedin a random-effects meta-analysis.

†

Esther M. González-Gil and Laia Peruchet-Noray equally contributed to thepresent work.
*Correspondence:
Esther M. González‑Gil
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
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González‑Gil et al. BMC Medicine(2024) 22:334

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Results Traditional anthropometric indexes, particularly BMI, WC, and weight were positively associated with CRP levels, in men and women. Body shape phenotypes also showed distinct associations with CRP. Specifically, PC2 showed
inverse associations with CRP in EPIC and UK Biobank in both sexes, similarly to height. PC3 was inversely associatedwith CRP among women, whereas positive associations were observed among men.
Conclusions Specific indexes of body size and body fat distribution showed differential associations with inflammation in adults. Notably, our results suggest that in women, height may mitigate the impact of a higher WC and HC
on inflammation. This suggests that subtypes of adiposity exhibit substantial variation in their inflammatory potential,
which may have implications for inflammation-related chronic diseases.
Keywords Body shape, Height, Anthropometric indicators, Inflammation, C-reactive protein

Background
Body fatness has been associated with various comorbidities and cardiometabolic risk [1]. Several studies suggested that abdominal obesity is more strongly associatedwith cardiovascular risk factors than general obesity usually proxied by body mass index (BMI) [2, 3]. Additionally, waist circumference (WC) is a proxy of abdominal
obesity positively associated with body size (mainly
weight and BMI, but also height). Thus, the measurementof WC may not provide information on the abdominal
deposition of fat beyond BMI.
To overcome the limitations of traditional anthropometric indexes, the allometric body shape index (ABSI)
and hip index (HI) [4, 5] were developed to reflect WC
and hip circumference (HC) among individuals with thesame weight and height [6]. Thus, information on bodysize is reflected in BMI and height and information on
body shape and fat distribution is reflected in ABSI andHI [7]. Alternatively, data-driven body shape phenotypes have been proposed to capture body size and bodyfat distribution holistically [8]. These phenotypes were
derived by a principal component (PC) analysis (PCA) of
six anthropometric traits [height, weight, BMI, WC, HC,
and waist-to-hip ratio (WHR)] [8]. Four body shape phenotypes were previously identified in the European Prospective Investigation into Cancer and Nutrition (EPIC)
study [9] and UK Biobank [10] characterizing overall
adiposity (PC1), tall stature with low WHR (PC2), tallstature with central obesity (PC3), and high BMI andweight with low WC and HC likely defining an athletic
phenotype (PC4). Previous findings suggested that thesecomplex anthropometric indexes (ABSI, HI, and body
shape phenotypes) may be more accurate than traditional
anthropometric indexes at capturing information on cardiometabolic and metabolic health [8], cancer risk assessment [9], and mortality risk stratification [11]. However,there is a limited number of studies on the association ofthese anthropometric indexes or body shape phenotypes
with inflammatory biomarkers.
Inflammation has been proposed as a potential biological mechanism linking body fatness to cardiometabolic

diseases and cancer [12, 13]. A previous study using datafrom UK Biobank reported positive associations of ABSI
and BMI with inflammatory biomarkers such as C-reactive protein (CRP), neutrophil, monocyte, and lymphocyte counts, while HI was only inversely associatedwith lymphocyte count [14]. Additionally, it has been
observed that a high ABSI value is an independent predictor of endothelial dysfunction in both sexes [15], suggesting its association with inflammatory processes.
Therefore, we used data from EPIC and UK Biobank
cohorts to examine cross-sectionally the associations of
the traditional anthropometric indexes, ABSI, HI, WHR,
waist-to-height ratio (WHtR), waist-to-hip index (WHI),
and four body shape phenotypes (PC1-4), with circulating inflammatory biomarkers.

Methods
Study population: EPIC and UK Biobank

EPIC
is
a
multicentre
cohortstudy
that
included ~ 520,000 participants, aged mainly between
35 and 65 years, recruited from 1992 to 2000 across ten
European countries (Denmark, France, Germany, Greece,
Italy, the Netherlands, Norway, Spain, Sweden, and theUK). Socio-demographic, dietary, lifestyle, anthropometric, and medical data were collected at recruitment
from all participants by country-specific questionnaires.
Further details have previously been described elsewhere [16]. Data from Greece were not available for thisanalysis.
Approximately 386,000 EPIC participants provided a
blood sample at recruitment [16]. All participants provided written informed consent and ethical approval forthe study was obtained from the International Agency
for Research on Cancer (IARC) ethics review panel (No.
20–34) and from all recruiting centres [16].
The UK Biobank is a prospective cohort study with over
500,000 individuals registered with the National HealthService of the United Kingdom [17]. Participants wererecruited between 2006 and 2010, aged 40 to 70, andfrom across 22 study centres in England, Scotland, andGonzález‑Gil et al. BMC Medicine(2024) 22:334

Wales. The study collected a wide range of data, including anthropometric measurements, lifestyle, healthrelated indexes, and biological samples.
Study population

In EPIC, participants were controls (i.e. cancer-free participants at blood collection) from nested case–control
or case-cohort sub-studies conducted on associations
between inflammation and cancer [18–28]. All included
participants had CRP measured as it was the main inclusion criteria and, some women among the included participants also had additional inflammatory biomarkers
measured including interleukin-6 (IL6), IL8, IL10, IL17d,
interleukin 1 receptor antagonist (IL1ra), tumour necrosis factor alpha (TNF-α), Interferon-gamma (IFN-γ),
leptin, and adiponectin. In total, 17,943 (69.1% women)
EPIC participants were included. However, the number of participants included for the analysis of each biomarker was variable due to data availability (Additional
file 1: Fig. S1).
The initial UK Biobank study sample included 503,386
individuals. After exclusion of participants with prevalent cancer at baseline and individuals lacking anthropometric measurements, 426,223 UK Biobank participants(53.2% women) who had values for CRP were included(Additional file 1: Fig. S1).
Assessment of anthropometric measurements

All anthropometric measurements included in EPIC followed standardized protocols, apart from Oxford (UK),
where body weight and height were self-reported. However, these self-reported data were improved via prediction equations in Oxford and the self-reported data wasconsidered valid for identifying associations [29]. Weight
was measured barefoot and rounded to the nearest 0.1 kg
whereas height was measured to the nearest 0.1 cm or
0.5 cm. WC was measured either between the lowest rib
and the iliac crest or at the narrowest circumference ofthe torso. HC was horizontally determined at the wider
point of the hip or above the buttocks. Additionally,
weight, WC and HC measurements were corrected for
clothing differences among centres [29]. When certain
anthropometric data were missing like the weight, height,
WC, and HC, they were imputed for some individuals
using centre-, age-, and sex-specific mean values.
In the UK Biobank, trained personnel collected anthropometric measurements, where Seca 202 stadiometer
and Tanita BC-418 body composition analyser were utilized to measure height and weight, respectively. WC wasassessed at the point of natural indentation or the umbilicus, while HC was gauged at the widest part of the participants’ hips.

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Anthropometric indexes and body shape phenotypes

BMI was computed as: weight in kg divided by height insquared metres. WHR was determined by dividing WC
by HC, both in centimeters. Additionally, other common
indexes were computed, including WHtR and WHI [14].
ABSI was calculated as WC/(BMI2/3height1/2) [30]
while HI was defined as HC (height/[height])0.310 (weight/[weight])−0.482 with squared brackets indicating the meanvalues of the sample [31]. The mean value for height in
EPIC was 164.9 cm while for weight was 70.9 kg whereas
in the UK Biobank mean height was 168.6 cm and weightwas 78.2 kg. Body shape phenotypes were previously
derived in EPIC and in UK Biobank [9, 10]. Body shapephenotypes were derived through PCA performed on
the standardized residuals of six anthropometric traits:height, weight, BMI, WC, HC, and WHR. For each single anthropometric trait, residuals were derived from a
linear regression model of the anthropometric trait onto
age, sex, and study centre [8–10]. This further adjustment
was performed to remove the additional variability introduced by both age and sex making those traits more comparable across populations. Six PCs were created where
each component represented a weighted sum of the six
transformed traits and was independent of the othercomponents. Out of the six PCs created, the first four
PCs collectively explained 99.8% of the total variationof the six anthropometric variables and were selected
for the subsequent analysis. Each of the four identified
PCs described a specific body morphology type: PC1,
overall adiposity, PC2, tall stature with low WHR, PC3,
tall stature with large WHR, and PC4 characterized byhigh BMI and weight with low WC and HC. Body shapephenotypes showed similar loadings between the twostudy samples (Additional file 1: Table S1). To facilitate
the interpretation of the body shape phenotypes, arithmetic means and SD of age and the six anthropometric
traits building the PCs for each study participant in the
5 and 95% of each PC are presented in Additional file 1:
Table S2 and S3.
Laboratory measurements

EPIC blood samples were collected (in fasting status in asmall proportion of the participants) and stored in liquid
nitrogen (− 196 °C) in a centralized biobank. For laboratory analysis, serum was used for most centres except for
Norway and Umea (Sweden) where citrated plasma andheparin plasma were used, respectively. Laboratory procedures for assessing inflammatory biomarkers in EPIC
have been already described in detail [18–28]. Values thatfell below LOD (Limit of Detection), below LLOQ (Lower
Limit of Quantification), or above ULOQ (Upper Limit of
Quantification) were set to LOD/2, LLOQ/2 and ULOQ,

González‑Gil et al. BMC Medicine(2024) 22:334

respectively. In the EPIC cohort, some biomarkers were
measured more than once in different nested case–control or case-cohort studies; in these instances, only datafrom the study with the highest number of quantified values for that biomarker were retained.
Within the UK Biobank, blood samples [32] were procured throughout the day (from 8 AM to 9 PM), devoidof any stipulated fasting prerequisites. CRP serum biomarker concentrations were quantified using the Beckman Coulter AU5800 analyser.
Statistical analysis

Concentrations of the biomarkers were transformed logarithmically to approximate a normal distribution. We
implemented a normalization step of the EPIC data as
described in a previously published pipeline [33]. Briefly,
linear mixed effect models were used for each biomarker. Models corrected for unwanted random effects
like variation due to analytical method and analyticalbatch and fixed effects such as study centre, fasting status (no, in between, yes) and time of blood draw (≤ 8 h,
(8–12 h], (12–16 h), > 16 h), while fixed effects of interest
such as anthropometric indexes, sex, and age at recruitment (years; continuous) were preserved. Linear regression models were used to confirm that normalization
removed unwanted sources of variation. All the normalized data were then standardized (mean = 0 and standard
deviation = 1) to rescale the concentration of the inflammatory biomarkers to enhance comparison.
The characteristics of participants were summarizedusing percentages for categorical variables and meanand standard deviation for continuous variables. Mann–
Whitney tests were used to evaluate differences by sex inboth cohorts. Spearman correlation coefficients between
anthropometric indexes and CRP were calculated separately for each sex in both studies.
We assessed associations of body shape phenotypes
and body fat distribution indexes with inflammatory biomarkers for both sexes in EPIC and UK Biobank using
linear regression models adjusted for age at recruitment(years, continuous), smoking status (never, current,
former, or unknown), education (primary education,
secondary/tech school, university degree or higher,
unknown) and physical activity (inactive, moderately
inactive, moderately active, active, and unknown in EPIC;
MET/week in UK Biobank). Results were combined separately for each sex through random-effects meta-analysis[34]. I2 was used to assess the proportion of total variability attributable to between-study heterogeneity.
We evaluated interactions of the body shape phenotypes and body fat distribution indexes with age (abovevs. below sex-specific median age) and smoking status(never vs. ever smoker), respectively, separately in men

Page 4 of 14

and women. Likelihood ratio tests were used to compare models with and without multiplicative interaction
terms. P values were reported non-adjusted and adjusted
for Bonferroni correction.
Sensitivity analyses among women were performed
with further adjustment for menopausal status (premenopausal or postmenopausal) and use of menopausal
hormonal treatment as well as stratification by menopausal status. Additionally, we performed sensitivity analyses excluding participants with cardiovascular diseases(myocardial infarction, stroke, or hypertension), andthose with diabetes at baseline in both study samples.
Further sensitivity analyses fitting regressions by sex
between anthropometric indexes and CRP levels additionally adjusting by ethnicity (White/mixed/Asian/
black/other/prefer not to answer) were performed toaccount for the potential variation introduced by the difference in anthropometric indexes across ethnic groups.
These analyses were restricted in UK Biobank given thelack of ethnicity information in EPIC. Finally, associations between obesity categories [obesity class I (BMI
30 −  < 35 kg/m2), obesity class II (BMI 35 −  < 40 kg/m2),
and obesity class III (BMI ≥ 40 kg/m2)] and CRP levels
were further investigated.
Sensitivity analyses also included regressions between
body shape phenotypes and CRP levels without adjustingby age, a variable already included in the construction of
the PCs. Additionally, correlations between the PCs andage have also been performed. These two analyses werereported to further check of potential bias due to overadjustment and collinearity.
Statistical analyses were performed using R 4.1.3. All
tests were two-sided and P value < 0.05 was considered
statistically significant. P values were adjusted by Bonferroni correction.

Results
The main characteristics of the study population are presented in Table 1. In EPIC, 69.1% of the participants were
women, whereas in UK Biobank women represented
53.2% of the study sample. Additionally, women in bothcohorts were generally younger than men. When it comesto anthropometric indexes, men in both EPIC and UK
Biobank had higher values compared to women, except
for HC and HI, where no such differences were observed
and where women had higher values, respectively. Additionally, a higher percentage of men were in the obesity
class I whereas a higher percentage of women were foundin the obesity classes II and III (Additional file 1: Table S4and S5). In both study samples, a greater proportion ofmen were current smokers, had attained higher levels of
education, and were more physically active.

González‑Gil et al. BMC Medicine(2024) 22:334

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Table 1 Main characteristics of the study populations: EPIC and UK Biobank
EPIC (n = 17,943)
Men

UK Biobank (n = 426,223)
Women

P value

Men

Women

P value

Mean (SD) or n %Mean (SD) or n %Sex5546 (30.9%)

12,397 (69.1%)< 0.001

199,506 (46.8%)

Mean (SD) or n % Mean (SD) or n %226,717 (53.2%)< 0.001Age (years)

53.1 (8.3)

52.5 (8.8)< 0.00156.4 (8.2)

56.1 (8.0)< 0.001

Height (cm)173.7 (7.3)

161.1 (6.7)< 0.001

175.7 (6.8)

162.5 (6.3)< 0.001

Weight (kg)80.8 (11.6)

66.6 (11.5)< 0.00185.9 (14.3)

71.4 (14.1)< 0.001< 0.001< 0.001

BMI (kg/m2)

26.8 (3.6)

25.7 (4.5)

27.8 (4.2)

27.1 (5.2)< 25 kg/m2

1787 (32.2%)

6221 (50.2%)

50,170 (25.1%)

90,233 (39.8%)

25–30 kg/m2

2812 (50.7%)4233 (34.1%)

98,707 (49.5%)

83,154 (36.7%)> 30 kg/m2

947 (17.1%)

1943 (15.7%)

50,629 (25.4%)

53,330 (23.5%)

Waist circumference (cm)95.1 (10.1)

81.2 (11.2)< 0.001

96.9 (11.3)

84.6 (12.5)< 0.001

Waist to Height ratio

0.5 (0.07)

0.6 (0.06)< 0.001

0.6 (0.1)

0.5 (0.1)< 0.001

Hip circumference (cm)101.0 (6.9)

101.6 (9.1)< 0.001

103.4 (7.6)

103.3 (10.4)< 0.001

Waist-to-hip ratio

0.9 (0.1)

0.8 (0.1)< 0.001

0.9 (0.1)

0.8 (0.1)< 0.001

Waist-to-hip index

4.1 (0.2)

3.6 (0.3)< 0.001

4.1 (0.2)

3.6 (0.3)< 0.001

ABSI

80.7 (4.3)

73.6 (5.0)< 0.001

79.8 (4.2)

73.9 (5.0)< 0.001

Hip index

96.7 (3.8)

104.3 (4.3)< 0.001100.5 (3.7)

107.2 (4.2)< 0.001

0.3

2.4 (4.2)

2.6 (4.3)< 0.001--

Inflammatory biomarkersa
CRP (µg/mL)

2.1 (3.07)

2.2 (35.8)

Interleukin 6 (pg/ml)-

1.1 (1.5)

Interleukin 8 (pg/ml)-

6.9 (65.6)--

Interleukin 10 (pg/ml)-

0.2 (0.4)--

Interleukin 17d (pg/ml)-

8.5 (15.6)--

IL1ra (pg/ml)-

162.9 (266.7)--

TNF-α (pg/ml)-

1.4 (2.1)--

IFN-γ (pg/ml)-

4.5 (10.4)--

Leptin (ng/ml)-

16.6 (14.2)--

Adiponectin (µg/ml)-

10.7 (5.2)--

Inactive

970 (17.5%)

3199 (25.8%)--

Moderately inactive

1734 (31.3%)4508 (36.4%)--

Cambridge physical activity index (EPIC)< 0.001

Moderately active

1330 (24.0%)2490 (20.1%)--

Active1423 (25.7%)2069 (16.7%)--

Unknown
Physical activity (MET-minutes/week) (UK Biobank)89 (1.6%)

131 (1.1%)

-

-

-

-

2821 (2963.2)

2516 (2467.5)

Smoking status< 0.001

Never

1676 (30.2%)7143 (57.6%)

97,847 (49.1%)

135,953 (60.0%)

Former2114 (38.1%)

2623 (21.2%)

75,634 (37.9%)

69,904 (30.9%)

Current
Unknown (EPIC)/prefer not to answer(UK Biobank)1692 (30.5%)2517 (20.3%)

25,015 (12.6%)

19,768 (8.7%)

64 (1.2%)

114 (0.9%)1010 (0.4%)1092 (0.4%)2164 (39.0%)5114 (41.3%)

33,638 (16.9%)

37,380 (16.5%)

Education
None/primary education< 0.001< 0.001

Secondary/Tech school

1862 (33.6%)4839 (39.0%)

48,209 (24.2%)

65,004 (28.7%)

University degree or higher

1420 (25.6%)

2184 (17.6%)

115,212 (57.7%)

121,780 (53.7%)

100 (1.8%)260 (2.1%)

2447 (1.2%)

2553 (1.1%)

Unknown (EPIC)/prefer not to answer(UK Biobank)< 0.001<0.001BMI Body mass index, ABSI A body shape index, CRP C-reactive protein, IL1ra Interleukin-1 receptor antagonist, TNF-α Tumour necrosis factor alpha, IFN-γ Interferongamma
Mann–Whitney tests were used to evaluate differences by sex in both cohorts. Significant when P value < 0.05a

All inflammatory biomarkers, except CRP, have missing values. See Additional file 1: Fig. 1

González‑Gil et al. BMC Medicine(2024) 22:334

Crude correlations between CRP and all the anthropometric indexes were similar in men and women inboth EPIC (Additional file 1: Fig. S2a) and UK Biobank(Additional file 1: Fig. S2b), being Bonferroni-adjusted.
Specifically, weight, BMI, WC, WHR, WHI, HC, WHtR,
and ABSI showed positive correlations with CRP,
whereas height was inversely correlated with CRP (Pvalue < 0.005). Additionally, HI showed weak correlationsin UK Biobank only. For the body shape phenotypes, PC1
showed positive correlations with CRP, whereas PC2 wasinversely correlated with CRP, in both cohorts and both
sexes. PC3 and PC4 showed weak or no correlation with
CRP.
Linear regression analyses adjusting for age, smoking
status, education, and physical activity levels showedthat the traditional anthropometric indexes and PC1(overall adiposity) were all positively associated with

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CRP, with stronger estimates than the other body shapeindexes in both cohorts (Figs. 1 and 2, Additional file 1:
Table S6 and S7) and in both men and women. Heightand PC2 (tall stature with low WHR) showed inverse
associations with CRP, with similar estimates in bothstudy samples and both sexes. Specifically, estimates
for the association between height and CRP in men
were -0.12 (95% CI: − 0.15, − 0.09) in EPIC and − 0.07
(95% CI: − 0.07, − 0.06) in UK Biobank, whereas inwomen were − 0.10 (95% CI: − 0.13, − 0.08) in EPIC
and − 0.12 (95% CI: − 0.13, − 0.12) in UK biobank.
Estimates for the association between PC2 and CRP
in men were − 0.09 (95% CI: − 0.12, − 0.07) in EPIC
and − 0.11 (95% CI: − 0.11, − 0.10) in UK Biobank, while
for women they were − 0.10 (95% CI: − 0.12, − 0.09) in
EPIC and − 0.13 (95% CI: − 0.14, − 0.13) in UK Biobank.
The other body shape phenotypes, PC3 (tall with highFig. 1 Associations between anthropometric indexes and C-reactive protein (CRP) among men in the EPIC study and UK Biobank. Adjusted for age
at recruitment, smoking status, education, and physical activity. Sample size EPIC: N = 5546, sample size UK Biobank: N = 199,506. BMI, body massindex; WHR, waist-to-hip ratio; ABSI, allometric body shape index. PC1, overall adiposity; PC2, tall with low waist-to-hip ratio; PC3, tall with highwaist-to-hip ratio, PC4, high BMI and weight with low waist circumference and hip circumference. X-axis represents the beta estimates and the 95%
confidence intervals (CI).

González‑Gil et al. BMC Medicine(2024) 22:334

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Fig. 2 Associations between anthropometric indexes and C-reactive protein (CRP) among women in the EPIC study and UK Biobank. Adjusted
for age at recruitment, smoking status, education, and physical activity. Sample size EPIC: N = 12,397, sample size UK Biobank: N = 226,717. BMI, bodymass index; WHR, waist-to-hip ratio; ABSI, allometric body shape index. PC1, overall adiposity; PC2, tall with low waist-to-hip ratio; PC3, tall with highwaist-to-hip ratio; PC4, high BMI and weight with low waist circumference and hip circumference. X-axis represents the beta estimates and the 95%
confidence intervals (CI).

WHR) and PC4 (high BMI and weight with low WC
and HC) were also inversely associated with CRP in
women (PC3: − 0.05; 95% CI: − 0.06, − 0.03 in EPIC
and − 0.10; 95% CI: − 0.11, − 0.10 in UK Biobank andPC4: − 0.02; 95% CI: − 0.04, 0.00 in EPIC and − 0.15;
95% CI: − 0.16, − 0.14 in UK Biobank). In contrast, positive associations with CRP were observed in men for
PC3: 0.03 (95% CI: 0.00, 0.06) in EPIC and 0.07 (95%
CI: 0.07, 0.08) in UK Biobank and PC4: 0.03 (95% CI:
0.01, 0.06) in EPIC and 0.18 (95% CI: 0.17, 0.20) in UK
Biobank. On the other hand, HI showed an inverse
association with CRP for men and a positive association
for women, but only consistent in UK Biobank: − 0.13
(95% CI: − 0.14, − 0.13) for men and 0.02 (95% CI: 0.01,
0.02) for women.

Similar estimates were found in the meta-analysis ofboth studies for both men and women. However, for HI(in men) and PC4, the meta-analysis showed less robust
estimates (Figs. 1 and 2 and Additional file 1: Table S8).
In most of the analyses, high heterogeneity was found(Additional file 1: Table S8).
Results for the other inflammatory biomarkers measured only in women from the EPIC study are presented
in Figs. 3 and 4 and Additional file 1: Table S9. Traditional
anthropometric indexes exhibited a greater number ofassociations and yielded higher effect estimates compared to the complex anthropometric indexes. Weight,
BMI, WC, and HC were positively associated with IL6,
IL1ra, TNF-α, and leptin, and inversely associated with
IL17d and adiponectin. No associations were foundGonzález‑Gil et al. BMC Medicine(2024) 22:334

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Fig. 3 Associations between simple anthropometric indexes and inflammatory biomarkers among women from the EPIC study. Adjusted
for age at recruitment, smoking status, education, and physical activity. BMI, body mass index; WHR, waist-to-hip ratio. X-axis represents the beta
estimates and the 95% confidence intervals (CI). IL6 (interleukin 6): n = 3315; IL8: n = 1415, IL10: n = 2036; IL17d: n = 1416; IL1ra (interleukin-1 receptor
antagonist): n = 1803; TNF-α (tumour necrosis factor alpha): n = 2425; IFN-γ (interferon-gamma): n = 1415; leptin: n = 2722; adiponectin: n = 3315

between the traditional anthropometric indexes and IL8or IL10. Both WHtR and WHR showed similar associations and were positively associated with IL8. WHR
was also positively associated with IFN-γ. Additionally,
ABSI was positively associated with IL6, IL1ra, TNF-α,and leptin and inversely associated with adiponectin. HI
showed positive associations with leptin and adiponectin.
For the body shape phenotypes, PC1 was positively associated with IL6, IL1ra, TNF-α, and leptin, and inversely
associated with IL17d and adiponectin. PC2 showed
inverse associations with IL6, IL1ra, and leptin and apositive association with adiponectin. PC3 was inversely
associated with leptin and adiponectin. PC4 showed noassociation with any inflammatory biomarker.

In sub-group analyses by age groups and smoking
status, estimated associations between anthropometric indexes and CRP were all in the same direction andsimilar in magnitude (Additional file 1: Table S10–S13).
However, several tests for multiplicative interaction may
reach statistical significance due to the large sample size,in particular in the UK Biobank.
Sensitivity analyses among women stratifying by menopausal status at recruitment showed similar resultsin both pre and postmenopausal women for the associations between anthropometric indexes and CRP in
both EPIC and UK Biobank (Additional file 1: Table S14and S15). Results were also similar when models were
further adjusted for menopausal hormonal treatment

González‑Gil et al. BMC Medicine(2024) 22:334

Page 9 of 14

Fig. 4 Associations between complex anthropometric indexes and inflammatory biomarkers among women from the EPIC study. Adjusted for age
at recruitment, smoking status, education, and physical activity. ABSI, allometric body shape index. X-axis represents the beta estimates and the 95%
confidence intervals (CI). PC1, overall adiposity; PC2, tall with low waist-to-hip ratio; PC3, tall with high waist-to-hip ratio; PC4, high BMI and weightwith low waist circumference and hip circumference. IL6 (interleukin 6): n = 3315; IL8: n = 1415, IL10: n = 2036; IL17d: n = 1416; IL1ra (interleukin-1
receptor antagonist): n = 1803; TNF-α (tumour necrosis factor alpha): n = 2425; IFN-γ (interferon-gamma): n = 1415; leptin: n = 2722; adiponectin:n = 3315(postmenopausal women only). The sensitivity analyses excluding participants with cardiovascular diseases(myocardial infarction, stroke, and hypertension) anddiabetes at baseline yielded similar results to those fromthe main analysis (Additional file 1: Table S16 and S17
for EPIC and Additional file 1: Table S18 and S19 for
UK Biobank). No differences in the associations by sex
between anthropometric indexes and CRP levels models additionally adjusted by ethnicity were found in UK
Biobank (Additional file 1: Table S20).
In an exploratory analysis, associations fitted byobesity class showed similar results in both EPIC

and UK Biobank presenting a higher and increasing estimate as the categories included higher
BMI ranges. Associations for all obesity categories
were higher in women than men (Additional file 1:
Table S4 and S5).
Finally, analyses excluding age as a confounder were
not showing differences in the results (Additional file 1:
Table S21 and S22). Slight inverse and positive correlations were found between PC1, PC3, and PC4 and age
among men and women in both cohorts, respectively(Additional file 1: Fig. S3).

González‑Gil et al. BMC Medicine(2024) 22:334

Discussion
In this study, we identified associations between various body shape phenotypes and body fat distribution
indexes and inflammatory biomarkers in both the EPIC
and UK Biobank cohorts, revealing significant genderspecific and phenotype-specific associations. Specifically,
we found positive associations between the traditional
anthropometric indexes (such as BMI, WC, and WHR),
ABSI, and PC1 (overall adiposity) and CRP, in both
sexes from EPIC and UK Biobank, as well as with IL6,
IL1ra, TNF-α, and leptin in women from the EPIC study(no data available for men). On the other hand, heightand PC2 (tall with low WHR) showed inverse associations with CRP in both study populations and with IL6,
IL1ra, and leptin in EPIC. PC3 (tall with high WHR) wasinversely associated with CRP among women but positively associated among men, in both cohorts. PC3 wasalso inversely associated with leptin and adiponectin inwomen of the EPIC study. The differential associations of
PC3 with CRP in men and women add a valuable layer ofunderstanding that extends beyond traditional measuresof adiposity. Additionally, our results suggest an inverse
association between height, independent of WC/WHR,
and inflammation, especially in women.
The positive associations found between traditional
anthropometric indexes and inflammation are in linewith existing literature [35]. However, the mechanismsthat cause the adipose tissue dysfunction triggering lowgrade inflammation are not yet fully understood [36, 37].
In addition, it has been observed that normal weightindividuals with abdominal fat accumulation can havemetabolic alterations whereas some individuals with obesity can be metabolically healthy [38–40]. However, there
is no consensus on the definition of metabolically healthyobesity and further research is needed as literature suggests that the clinical relevance of the associations withmetabolic dysfunction is speculative, and mostly basedon cross-sectional studies. In this sense, a study using
data from the UK Biobank [41] indicated that individuals
classified as metabolically healthy obese still face elevatedrisks of cardiovascular diseases, respiratory disease, diabetes, and all-cause mortality compared to individuals
without obesity and without obesity-related risk factors.
Therefore, it would be useful to consider complex
anthropometric indexes over single indexes as they may
better reflect body fat distribution and could better capture inflammatory status [31]. From the results of ourstudy, BMI, WC, and WHR are interchangeable and do
not give additional insight on the association with CRPor IL6.
Two of the most used complex anthropometric indexes,
ABSI and HI, have previously shown associations with
inflammatory biomarkers [14], endothelial dysfunction

Page 10 of 14[15], and cardiometabolic risk factors [42]. However,literature examining associations of those indexes andinflammatory markers is scarce and focused exclusively
on ABSI, rarely considering HI. Large ABSI has also been
associated with higher CRP in previous studies [43, 44],
and, in agreement with our findings, BMI has also shownstronger positive associations with CRP in women compared to men [44]. In our study, ABSI showed stronger
associations with CRP than HI suggesting that ABSI ismore closely linked to inflammatory status. A previous
study in UK Biobank [14] found that BMI and ABSI werepositively associated with CRP showing similar results
to ours. However, no associations were found for HI and
CRP in that study. Even if BMI seems to show greater
associations with inflammation than ABSI, they are independent by design and should be interpreted as complementary. Thus, our results suggest that using complex
anthropometric indexes, beyond traditional metrics like
BMI, into clinical practice could improve the identification of individuals at higher risk of inflammation-related
conditions. This could lead to earlier interventions andbetter clinical outcomes.
Among the body shape phenotypes, we found that PC1(overall adiposity) was the one more correlated with single anthropometric indexes. This is expected as PC1 isconstructed based on higher loadings in BMI, weight,
HC, and WC. Likewise, PC1 and all these anthropometric indexes showed similar associations with CRP. On
the other hand, PC2 (tall stature with low WHR) consistently exhibited inverse associations with CRP acrossboth cohorts and sexes. In this sense, the multivariate
approach of a PCA simplifies to holistically account fordifferent traditional anthropometric indexes, like adiposity and height. Thus, body shape phenotypes may better reflect body composition than individual indexes forrisk assessment and incorporated into clinical practice
could help refine models for inflammation-related diseases, enabling more personalized and effective intervention strategies. Previous findings have suggested that
WHR may be positively linked to CRP [45], irrespectiveof age, sex, metabolic and various lifestyle factors in several studies. However, we showed that PC3 (tall stature
with high WHR) was inversely associated with concentrations of CRP in women. Overall, our results for PC2,
in both sexes, and PC3, in women, suggest that height
mitigates the positive association of abdominal adiposity with CRP, especially in women. Women generally
have a higher proportion of body fat compared to men[46], even when matched for BMI, and this physiologicaldifference also could contribute to variations in inflammatory markers. These observed associations highlightthe importance of tailored public health initiatives that

González‑Gil et al. BMC Medicine(2024) 22:334

address gender-specific risk factors and promote lifestylemodifications to mitigate inflammation and its related
conditions.
Evidence suggests that height is positively associatedwith cancer risk [47–50], but also inversely associatedwith cardiometabolic risk [51] and diabetes [52], for thelatter also when adjusting for WC. Although BMI and
WC are established variables with which to assess morbidity and mortality risk, adult height is also associatedwith mortality independently of adiposity measures [53].
Previous research supports that shorter height is positively associated with cardiovascular risk [54] and that
this risk might at least in part be mediated by cardiometabolic risk factors relevant for type 2 diabetes, namely
blood pressure, blood lipids, and inflammation [55, 56].
Also, in one analysis that studied a sub-sample of the
EPIC study, height appeared to be more strongly correlated with CRP among women, playing a stronger role in
the association with diabetes in women than in men [52].
These results suggest taller height in women might havesome metabolic implications. This stronger inverse association of taller height and CRP in women could explain
the divergent result for PC3 as, for women, height can bea major driver than WHR of the association with CRP.
However, more studies in different cohorts are needed tosupport these findings.
In our study based on two cohorts, height emerged as
an indicator inversely associated with CRP in both sexes,
even in combination with abdominal adiposity in womenfrom both studies. We found similar results when considering menopausal status or when excluding those withcardiovascular diseases or diabetes. Furthermore, height
was inversely associated with IL6 and leptin in womenfrom EPIC. These inverse associations suggest that height
could be protective for inflammatory status.
Strengths and limitations

The primary strength of our study is the novel results onanthropometric indexes and inflammation in two large
cohort studies: EPIC and UK Biobank. These studies
were conducted following standardized procedures. The
use of several anthropometric indexes is another major
strength as it allows comparison between them. Also,
we performed sensitivity analyses, excluding those withdiabetes and cardiovascular diseases to detect potential
shared molecular pathways with adiposity that could bias
our results.
The main limitation of our study is the lack of data forsome of the inflammatory biomarkers in UK Biobank
and in men in EPIC. Furthermore, we cannot determine
subcutaneous adipose tissue from overall adipose tissue
as we have no body composition data using objective

Page 11 of 14

techniques. Also, the cross-sectional design of the study
does not allow us to establish causal relationships while
one-time-point measurements are not ideal to assess
temporal relationships. However, this study could be considered a starting point for further longitudinal studies.

Conclusions
This study adds to our understanding of the relationship between body composition and systemic inflammation. Our findings affirm an inverse relationship between
height and inflammation. Notably, our results suggestthat a taller height may mitigate the impact of a large
WC or a large HC on inflammation in women. PC2 (tallstature with low WHR) and PC3 (tall stature with large
WHR) were inversely associated with CRP in both the
EPIC and UK Biobank studies. These findings underscorethe value of body shape phenotypes suggesting that they
could serve as additional tools for risk stratification in
epidemiological studies as well as to improve personalized approaches to disease prevention and management.
Further longitudinal studies that include more inflammatory markers are needed.
Abbreviations
ABSI	Allometric body shape index
BMI	Body mass index
CRP	C-reactive protein
EPIC	European Prospective Investigation into Cancer and Nutrition studyHC	Hip circumferenceHI	Hip index
IARC​	International Agency for Research on Cancer
IFN-γ	Interferon gammaIL	Interleukin
LLOQ	Lower limit of quantification
LOD	Limit of detection
PC1	Overall adiposity
PC2	Tall stature with low WHR
PC3	Tall and with high WHR
PC4	High BMI and weight with low WC and HC
TNF-α	Tumour necrosis factor alphaULOQ	Upper Limit of Quantification
WC	Waist circumference
WHR	Waist-to-hip ratio
WHtR	Waist-to-height ratio

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03544-3.
Additional file 1: Fig. S1. Flowchart with the included number of subjects
in EPIC and UK Biobank. IL1ra: Interleukin-1 receptor antagonist; TNF-α:
Tumour necrosis factor alpha; IFN-γ: Interferon-gamma. Fig. S2a. Correlations between adiposity and concentration of C-reactive protein in the
EPIC study by sex. Fig. S2b. Correlations between adiposity and concentration of C-reactive protein in UK Biobank study by sex. Fig. S3. Correlations
between body shape phenotypes and age in the EPIC and UK Biobank
study by sex. Table S1. Loadings of the principal components derived in
EPIC (n = 17,943) and UK Biobank (n = 426,223). Table S2. Mean and standard deviations of age and the six anthropometric traits building the body
shape phenotypes for individuals included in the top and bottom 5% ofeach PC in the EPIC study. Table S3. Mean and standard deviations of age
and the six anthropometric traits building the body shape phenotypes forGonzález‑Gil et al. BMC Medicine(2024) 22:334

individuals included in the top and bottom 5% of each PC in UK Biobank
study. Table S4. Sample size and associations between individuals withobesity and by obesity class and C-reactive protein (CRP) levels in EPIC.
Table S5. Sample size and associations between individuals with obesityand by obesity class and C-reactive protein (CRP) levels in UK Biobank.
Table S6. Associations between anthropometric indexes and C-reactive
protein (CRP) in the EPIC study. Table S7. Associations between anthropometric indexes and C-reactive protein (CRP) in UK Biobank study. Table S8.
Meta-analysis results of the associations between anthropometric indexes
and C-reactive protein (CRP) in the EPIC and UK Biobank cohorts. Table S9.
Associations between anthropometric indexes and all the inflammatory
biomarkers analysed in EPIC. Table S10. Associations between anthropometric indexes and C-reactive protein (CRP) stratifying by sex-specificmedian age and p-values for likelihood ratio test comparing models
without and with interactions in the EPIC study. Table S11. Associations
between anthropometric indexes and C-reactive protein (CRP) stratifying
by smoking status and p-values for likelihood ratio test comparing models
without and with interactions in the EPIC study. Table S12. Associations
between anthropometric indexes and C-reactive protein (CRP) stratifying
by sex-specific median age and p-values for likelihood ratio test comparing models without and with interactions in UK Biobank study. Table S13.
Associations between anthropometric indexes and C-reactive protein(CRP) stratifying by smoking status and p-values for likelihood ratio testcomparing models without and with interactions in UK Biobank study.
Table S14. Associations between anthropometric indexes and C-reactive
protein (CRP) in the EPIC study. Data presented for all women and bymenopausal status. Table S15. Associations between anthropometricindexes and C-reactive protein (CRP) in UK Biobank study. Data presented
for all women and by menopausal status. Table S16. Associations between
anthropometric indexes and C-reactive protein (CRP) in the EPIC study
excluding 3,643 participants with CVD at baseline defined as myocardial
infarction (heart attack), stroke or hypertension. Table S17. Associations
between anthropometric indexes and C-reactive protein (CRP) in the EPIC
Study excluding 484 participants with diabetes at baseline. Table S18.
Associations between anthropometric indexes and C-reactive protein(CRP) in UK Biobank excluding 102,020 participants with CVD at baseline
defined as myocardial infarction (heart attack), stroke, or hypertension.
Table S19. Associations between anthropometric indexes and C-reactive
protein (CRP) in UK Biobank excluding 26,365 participants with diabetes
at baseline. Table S20. Associations between anthropometric indexes andC-reactive protein (CRP) in UK Biobank additionally adjusted for ethnicity.
Table S21. Associations between body shape phenotypes and C-reactive
protein (CRP) in the EPIC study excluding age as a confounder. Table S22.
Associations between anthropometric indexes and C-reactive protein(CRP) in UK Biobank study excluding age as a confounder.
Acknowledgements
The authors thank all EPIC participants and staff for their outstanding contribution to the study. This research has been conducted using the UK Biobank
Resource under Application Number 55870 and we express our gratitude to
the participants and those involved in building the resource.
IARC disclaimer
Where authors are identified as personnel of the International Agency for
Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on
Cancer/World Health Organization.
Authors’ contributions
EMGG, and LPN analysed the data. EMGG, LPN, AS, SC, CB, AN, YMS, RFJ, PAE,
HB, MG, MV, JMAB, AO, AT, VS, CCE, DA, AKH, MG, SMCY, NRZ, CCD, ELL, MBS,DP, SS, FR, GM, TK, VV, SR, HF and LD have drafted the manuscript or substantively revised it. LD conceived and designed the work. EMGG, LPN, SC, HF, andLD contributed to the interpretation of the data. All authors have read andapproved the submitted manuscript.

Page 12 of 14

Authors’ Twitter handles
Twitter handles: @GonzalezGilEM (Esther M. González-Gil), @LPeruchet (Laia
Peruchet-Noray), @MS_Yahya_ (Yahya Mahamat-Saleh), @HFreisling (Heinz
Freisling).
Funding
This work was supported by the German Research Fund (DFG) [BA 5459/2–1]
and the French National Cancer Institute (l’Institut National du Cancer)[INCa_16643].
The coordination of EPIC is financially supported by the International Agency
for Research on Cancer (IARC) and by the Department of Epidemiology andBiostatistics, School of Public Health, Imperial College London, which has
additional infrastructure support provided by the NIHR Imperial Biomedical
Research Centre (BRC). The national cohorts are supported by:Danish Cancer
Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle
Générale de l’Education Nationale, Institut National de la Santé et de laRecherche Médicale (INSERM) (France); German Cancer Aid, German Cancer
Research Centre (DKFZ), German Institute of Human Nutrition PotsdamRehbruecke (DIfE), Federal Ministry of Education and Research(BMBF) (Germany);
Associazione Italiana per la Ricerca sul CancroAIRCItaly,Compagnia di
SanPaolo and National Research Council (Italy); Dutch Ministryof Public Health,Welfare and Sports (VWS), Netherlands Cancer Registry (NKR),LK ResearchFunds, Dutch Prevention Funds, Dutch ZON (Zorg OnderzoekNederland),World Cancer Research Fund (WCRF), Statistics Netherlands(The Netherlands);
Health Research Fund (FIS)—Instituto de Salud Carlos III(ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murciaand Navarra, and theCatalan Institute of Oncology—ICO (Spain); SwedishCancer Society, Swedish
Research Council and County Councils of Skåne andVästerbotten (Sweden);Cancer Research UK (14136 to EPICNorfolk; C8221/A29017 to EPICOxford),
Medical Research Council (1000143 to EPIC Norfolk;MR/M012190/1 to EPICOxford) (United Kingdom). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The UK Biobank was supported by the Wellcome Trust, Medical ResearchCouncil, Department of Health, Scottish Government and the Northwest
Regional Development Agency. It has also had funding from the Welsh
Assembly Government and the British Heart Foundation.
Availability of data and materials
EPIC data are available for investigators who seek to answer importantquestions on health and disease in the context of research projects that areconsistent with the legal and ethical standard practices of IARC/WHO and the
EPIC centres. The primary responsibility for accessing the data belongs to IARC
and the EPIC centres. For information on how to submit an application for
gaining access to EPIC data and/or biospecimens, please follow the instructions at https://​epic.​iarc.​fr/​access/.
The data that support the findings of this study are available from UK Biobank
but restrictions apply to their availability. You could also use this statement:
UK Biobank is an open access resource. Bona fide researchers can apply to use
the UK Biobank dataset by registering and applying at http://​ukbio​bank.​ac.​uk/​
regis​ter-​apply/. These data were used under licence for the current study, andso are not publicly available. The data are, however, available from the authors
upon reasonable request and with permission of UK Biobank.

Declarations
Ethics approval and consent to participate
This study complies with the Declaration of Helsinki. EPIC was approved by
the Ethics Committee of the International Agency for Research on Cancer(IARC) (ref IEC 14–02), Lyon, France, as well as the local ethics committees ofthe study centres. All participants provided written informed consent for datacollection and storage as well as individual follow-up.
The UK Biobank study was approved by the Northwest Multi-Centre Research
Ethics Committee (reference for UK Biobank is 16/NW/0274) and all participants provided written informed consent to participate in the UK Biobank
study. The study protocol is available online (https://​www.​ukbio​bank.​ac.​uk/​
media/​gnkey​h2q/​study-​ratio​nale.​pdf ).
Consent for publication
Not applicable.

González‑Gil et al. BMC Medicine(2024) 22:334

Page 13 of 14

Competing interests
The authors declare no competing interests.

5.

Author details
1
Nutrition and Metabolism Branch, International Agency for Research On Cancer, World Health Organization, 69372 Lyon, CEDEX 08, France. 2 Departmentof Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona,
Spain. 3 Department of Epidemiology and Preventive Medicine, Universityof Regensburg, Regensburg, Germany. 4 Center for Translational Oncology,
University Hospital Regensburg, Regensburg, Germany. 5 Bavarian Cancer
Research Center (BZKF), Regensburg, Germany. 6 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London,
London, UK. 7 Instituto de Salud Pública y Laboral de Navarra, 31003 Pamplona, Spain. 8 Centro de Investigación Biomédica en Red de Epidemiología
y Salud Pública (CIBERESP), 28029 Madrid, Spain. 9 Navarra Institute for HealthResearch (IdiSNA), 31008 Pamplona, Spain. 10 Sub Directorate for Public Healthand Addictions of Gipuzkoa, Ministry of Health of the Basque Government,
San Sebastian, Spain. 11 Epidemiology of Chronic and Communicable Diseases
Group, BioGipuzkoa (BioDonostia) Health Research Institute, San Sebastián,Spain. 12 Centre forPrevention, Lifestyle and Health, National Institute for Public
Health and the Environment, PO Box 1, 3720 BA Bilthoven, the Netherlands.
13
Julius Center for Health Sciences and Primary Care, University Medical CenterUtrecht, Utrecht, The Netherlands. 14 Danish Cancer Institute, Danish Cancer
Society, Diet, Cancer and Health Strandboulevarden 49 2100, Copenhagen,Denmark. 15 Department of Public Health, University of Aarhus, Copenhagen, Denmark. 16 Department of Public Health, University of Copenhagen,Copenhagen, Denmark. 17 Dipartimento Di Salute Mentale E Fisica E Medicina
Preventiva, Università Degli Studi Della Campania ‘Luigi Vanvitelli’, Napoli, Italy.
18
Unit of Nutrition and Cancer, Catalan Institute of Oncology-ICO, L’Hospitalet
de Llobregat, Barcelona, Spain. 19 Nutrition and Cancer Group, Epidemiology, Public Health, Cancer Prevention and Palliative Care Program, Bellvitge
Biomedical Research Institute-IDIBELL, L’Hospitalet de Llobregat, Barcelona,
Spain. 20 Department of Research, The Cancer Registry of Norway, Oslo, Norway. 21 Department of Nutrition, Oslo New University College, Oslo, Norway.
22
Department of Epidemiology, Murcia Regional Health Council-IMIB, Murcia,
Spain. 23 Research Group On Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia. 24 Department of Public
Health, Aarhus University, Aarhus, Denmark. 25 Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal,
Germany. 26 German Center for Diabetes Research (DZD), Munchen‑Neuherberg, Germany. 27 Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany. 28 Escuela Andaluza de Salud Pública (EASP), 18011 Granada,
Spain. 29 Instituto de Investigación Biosanitaria Ibs, GRANADA, 18012 Granada,
Spain. 30 Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori Di Milano, Milan, Italy. 31 Centre for Biostatistics, Epidemiology,
and Public Health (C‑BEPH) Department of Clinical and Biological Sciences,
University of Turin, Turin, Italy. 32 Clinical Epidemiology Unit, Institute for Cancer
Research, Prevention, and Clinical Network (ISPRO), Florence, Italy. 33 Cancer
Epidemiology Unit, Nuffield Department of Population Health, Universityof Oxford, Oxford, UK.

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18.

Received: 4 March 2024 Accepted: 29 July 2024
19.
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